Model hyperparameter adjustment using vehicle driving context classification

ABSTRACT

In some aspects, a device of a vehicle may obtain information relating to an environment in which the vehicle is located. The device may determine using a machine learning model, a driving context of the vehicle based at least in part on the information relating to the environment, and a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the driving context. The device may determine, using the model configured with the set of hyperparameters, the driving behavior for the vehicle. The device may cause autonomous operation of the vehicle in accordance with the driving behavior. Numerous other aspects are described.

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to wirelesscommunication and to techniques and apparatuses for model hyperparameteradjustment using vehicle driving context classification.

BACKGROUND

Wireless communication systems are widely deployed to provide varioustelecommunication services such as telephony, video, data, messaging,and broadcasts. Typical wireless communication systems may employmultiple-access technologies capable of supporting communication withmultiple users by sharing available system resources (e.g., bandwidth,transmit power, or the like). Examples of such multiple-accesstechnologies include code division multiple access (CDMA) systems, timedivision multiple access (TDMA) systems, frequency division multipleaccess (FDMA) systems, orthogonal frequency division multiple access(OFDMA) systems, single-carrier frequency division multiple access(SC-FDMA) systems, time division synchronous code division multipleaccess (TD-SCDMA) systems, and Long Term Evolution (LTE).LTE/LTE-Advanced is a set of enhancements to the Universal MobileTelecommunications System (UMTS) mobile standard promulgated by theThird Generation Partnership Project (3GPP).

A wireless network may include one or more base stations that supportcommunication for a user equipment (UE) or multiple UEs. A UE maycommunicate with a base station via downlink communications and uplinkcommunications. “Downlink” (or “DL”) refers to a communication link fromthe base station to the UE, and “uplink” (or “UL”) refers to acommunication link from the UE to the base station.

The above multiple access technologies have been adopted in varioustelecommunication standards to provide a common protocol that enablesdifferent UEs to communicate on a municipal, national, regional, and/orglobal level. New Radio (NR), which may be referred to as 5G, is a setof enhancements to the LTE mobile standard promulgated by the 3GPP. NRis designed to better support mobile broadband internet access byimproving spectral efficiency, lowering costs, improving services,making use of new spectrum, and better integrating with other openstandards using orthogonal frequency division multiplexing (OFDM) with acyclic prefix (CP) (CP-OFDM) on the downlink, using CP-OFDM and/orsingle-carrier frequency division multiplexing (SC-FDM) (also known asdiscrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, aswell as supporting beamforming, multiple-input multiple-output (MIMO)antenna technology, and carrier aggregation. As the demand for mobilebroadband access continues to increase, further improvements in LTE, NR,and other radio access technologies remain useful.

SUMMARY

Some aspects described herein relate to a method. The method may includeobtaining, by a device of a vehicle, information relating to anenvironment in which the vehicle is located. The method may includedetermining, by the device using a machine learning model, a drivingcontext of the vehicle based at least in part on the informationrelating to the environment, and a set of hyperparameters for a model,that is used to determine a driving behavior for the vehicle, based atleast in part on the driving context. The method may includedetermining, by the device and using the model configured with the setof hyperparameters, the driving behavior for the vehicle. The method mayinclude causing, by the device, autonomous operation of the vehicle inaccordance with the driving behavior.

Some aspects described herein relate to a system for autonomous drivingof a vehicle. The system may include a memory and one or more processorscoupled to the memory. The one or more processors may be configured toobtain information relating to an environment in which the vehicle islocated. The one or more processors may be configured to determine,using a machine learning model, a driving context of the vehicle basedat least in part on the information relating to the environment, and aset of hyperparameters for a model, that is used to determine a drivingbehavior for the vehicle, based at least in part on the driving context.The one or more processors may be configured to determine, using themodel configured with the set of hyperparameters, the driving behaviorfor the vehicle. The one or more processors may be configured to causeautonomous operation of the vehicle in accordance with the drivingbehavior.

Some aspects described herein relate to a non-transitorycomputer-readable medium that stores a set of instructions by a device.The set of instructions, when executed by one or more processors of thedevice, may cause the device to obtain information relating to anenvironment in which a vehicle is located. The set of instructions, whenexecuted by one or more processors of the device, may cause the deviceto determine, using a machine learning model, a driving context of thevehicle based at least in part on the information relating to theenvironment, and a set of hyperparameters for a model, that is used todetermine a driving behavior for the vehicle, based at least in part onthe driving context. The set of instructions, when executed by one ormore processors of the device, may cause the device to configure themodel with the set of hyperparameters based at least in part ondetermining the set of hyperparameters.

Some aspects described herein relate to an apparatus. The apparatus mayinclude means for obtaining information relating to an environment inwhich a vehicle is located. The apparatus may include means fordetermining, using a machine learning model, a set of hyperparametersfor a model, that is used to determine a driving behavior for thevehicle, based at least in part on the information relating to theenvironment. The apparatus may include means for configuring the modelwith the set of hyperparameters based at least in part on determiningthe set of hyperparameters.

Aspects generally include a method, apparatus, system, computer programproduct, non-transitory computer-readable medium, user equipment, basestation, wireless communication device, and/or processing system assubstantially described herein with reference to and as illustrated bythe drawings and specification.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described hereinafter. The conceptionand specific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the scope of the appended claims. Characteristics of theconcepts disclosed herein, both their organization and method ofoperation, together with associated advantages, will be betterunderstood from the following description when considered in connectionwith the accompanying figures. Each of the figures is provided for thepurposes of illustration and description, and not as a definition of thelimits of the claims.

While aspects are described in the present disclosure by illustration tosome examples, those skilled in the art will understand that suchaspects may be implemented in many different arrangements and scenarios.Techniques described herein may be implemented using different platformtypes, devices, systems, shapes, sizes, and/or packaging arrangements.For example, some aspects may be implemented via integrated chipembodiments or other non-module-component based devices (e.g., end-userdevices, vehicles, communication devices, computing devices, industrialequipment, retail/purchasing devices, medical devices, and/or artificialintelligence devices). Aspects may be implemented in chip-levelcomponents, modular components, non-modular components, non-chip-levelcomponents, device-level components, and/or system-level components.Devices incorporating described aspects and features may includeadditional components and features for implementation and practice ofclaimed and described aspects. For example, transmission and receptionof wireless signals may include one or more components for analog anddigital purposes (e.g., hardware components including antennas, radiofrequency (RF) chains, power amplifiers, modulators, buffers,processors, interleavers, adders, and/or summers). It is intended thataspects described herein may be practiced in a wide variety of devices,components, systems, distributed arrangements, and/or end-user devicesof varying size, shape, and constitution.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the above-recited features of the present disclosure can beunderstood in detail, a more particular description, briefly summarizedabove, may be had by reference to aspects, some of which are illustratedin the appended drawings. It is to be noted, however, that the appendeddrawings illustrate only certain typical aspects of this disclosure andare therefore not to be considered limiting of its scope, for thedescription may admit to other equally effective aspects. The samereference numbers in different drawings may identify the same or similarelements.

FIG. 1 is a diagram illustrating an example of a wireless network, inaccordance with the present disclosure.

FIG. 2 is a diagram illustrating an example of a base station incommunication with a user equipment (UE) in a wireless network, inaccordance with the present disclosure.

FIGS. 3A-3B are diagrams illustrating an example of model hyperparameteradjustment using vehicle driving context classification, in accordancewith the present disclosure.

FIG. 4 is a diagram illustrating an example process associated withmodel hyperparameter adjustment using vehicle driving contextclassification, in accordance with the present disclosure.

FIG. 5 is a diagram of an example apparatus, in accordance with thepresent disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully hereinafterwith reference to the accompanying drawings. This disclosure may,however, be embodied in many different forms and should not be construedas limited to any specific structure or function presented throughoutthis disclosure. Rather, these aspects are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope of the disclosure to those skilled in the art. One skilled in theart should appreciate that the scope of the disclosure is intended tocover any aspect of the disclosure disclosed herein, whether implementedindependently of or combined with any other aspect of the disclosure.For example, an apparatus may be implemented or a method may bepracticed using any number of the aspects set forth herein. In addition,the scope of the disclosure is intended to cover such an apparatus ormethod which is practiced using other structure, functionality, orstructure and functionality in addition to or other than the variousaspects of the disclosure set forth herein. It should be understood thatany aspect of the disclosure disclosed herein may be embodied by one ormore elements of a claim.

Several aspects of telecommunication systems will now be presented withreference to various apparatuses and techniques. These apparatuses andtechniques will be described in the following detailed description andillustrated in the accompanying drawings by various blocks, modules,components, circuits, steps, processes, algorithms, or the like(collectively referred to as “elements”). These elements may beimplemented using hardware, software, or combinations thereof. Whethersuch elements are implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem.

While aspects may be described herein using terminology commonlyassociated with a 5G or New Radio (NR) radio access technology (RAT),aspects of the present disclosure can be applied to other RATs, such asa 3G RAT, a 4G RAT, and/or a RAT subsequent to 5G (e.g., 6G).

FIG. 1 is a diagram illustrating an example of a wireless network 100,in accordance with the present disclosure. The wireless network 100 maybe or may include elements of a 5G (e.g., NR) network and/or a 4G (e.g.,Long Term Evolution (LTE)) network, among other examples. The wirelessnetwork 100 may include one or more base stations 110 (shown as a BS 110a, a BS 110 b, a BS 110 c, and a BS 110 d), a user equipment (UE) 120 ormultiple UEs 120 (shown as a UE 120 a, a UE 120 b, a UE 120 c, a UE 120d, and a UE 120 e), and/or other network entities. A base station 110 isan entity that communicates with UEs 120. A base station 110 (sometimesreferred to as a BS) may include, for example, an NR base station, anLTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G),an access point, and/or a transmission reception point (TRP). Each basestation 110 may provide communication coverage for a particulargeographic area. In the Third Generation Partnership Project (3GPP), theterm “cell” can refer to a coverage area of a base station 110 and/or abase station subsystem serving this coverage area, depending on thecontext in which the term is used.

A base station 110 may provide communication coverage for a macro cell,a pico cell, a femto cell, and/or another type of cell. A macro cell maycover a relatively large geographic area (e.g., several kilometers inradius) and may allow unrestricted access by UEs 120 with servicesubscriptions. A pico cell may cover a relatively small geographic areaand may allow unrestricted access by UEs 120 with service subscription.A femto cell may cover a relatively small geographic area (e.g., a home)and may allow restricted access by UEs 120 having association with thefemto cell (e.g., UEs 120 in a closed subscriber group (CSG)). A basestation 110 for a macro cell may be referred to as a macro base station.A base station 110 for a pico cell may be referred to as a pico basestation. A base station 110 for a femto cell may be referred to as afemto base station or an in-home base station. In the example shown inFIG. 1 , the BS 110 a may be a macro base station for a macro cell 102a, the BS 110 b may be a pico base station for a pico cell 102 b, andthe BS 110 c may be a femto base station for a femto cell 102 c. A basestation may support one or multiple (e.g., three) cells.

In some examples, a cell may not necessarily be stationary, and thegeographic area of the cell may move according to the location of a basestation 110 that is mobile (e.g., a mobile base station). In someexamples, the base stations 110 may be interconnected to one anotherand/or to one or more other base stations 110 or network nodes (notshown) in the wireless network 100 through various types of backhaulinterfaces, such as a direct physical connection or a virtual network,using any suitable transport network.

The wireless network 100 may include one or more relay stations. A relaystation is an entity that can receive a transmission of data from anupstream station (e.g., a base station 110 or a UE 120) and send atransmission of the data to a downstream station (e.g., a UE 120 or abase station 110). A relay station may be a UE 120 that can relaytransmissions for other UEs 120. In the example shown in FIG. 1 , the BS110 d (e.g., a relay base station) may communicate with the BS 110 a(e.g., a macro base station) and the UE 120 d in order to facilitatecommunication between the BS 110 a and the UE 120 d. A base station 110that relays communications may be referred to as a relay station, arelay base station, a relay, or the like.

The wireless network 100 may be a heterogeneous network that includesbase stations 110 of different types, such as macro base stations, picobase stations, femto base stations, relay base stations, or the like.These different types of base stations 110 may have different transmitpower levels, different coverage areas, and/or different impacts oninterference in the wireless network 100. For example, macro basestations may have a high transmit power level (e.g., 5 to 40 watts)whereas pico base stations, femto base stations, and relay base stationsmay have lower transmit power levels (e.g., 0.1 to 2 watts).

A network controller 130 may couple to or communicate with a set of basestations 110 and may provide coordination and control for these basestations 110. The network controller 130 may communicate with the basestations 110 via a backhaul communication link. The base stations 110may communicate with one another directly or indirectly via a wirelessor wireline backhaul communication link.

The UEs 120 may be dispersed throughout the wireless network 100, andeach UE 120 may be stationary or mobile. A UE 120 may include, forexample, an access terminal, a terminal, a mobile station, and/or asubscriber unit. A UE 120 may be a cellular phone (e.g., a smart phone),a personal digital assistant (PDA), a wireless modem, a wirelesscommunication device, a handheld device, a laptop computer, a cordlessphone, a wireless local loop (WLL) station, a tablet, a camera, a gamingdevice, a netbook, a smartbook, an ultrabook, a medical device, abiometric device, a wearable device (e.g., a smart watch, smartclothing, smart glasses, a smart wristband, smart jewelry (e.g., a smartring or a smart bracelet)), an entertainment device (e.g., a musicdevice, a video device, and/or a satellite radio), a vehicular componentor sensor, a smart meter/sensor, industrial manufacturing equipment, aglobal positioning system device, and/or any other suitable device thatis configured to communicate via a wireless medium.

Some UEs 120 may be considered machine-type communication (MTC) orevolved or enhanced machine-type communication (eMTC) UEs. An MTC UEand/or an eMTC UE may include, for example, a robot, a drone, a remotedevice, a sensor, a meter, a monitor, and/or a location tag, that maycommunicate with a base station, another device (e.g., a remote device),or some other entity. Some UEs 120 may be considered Internet-of-Things(IoT) devices, and/or may be implemented as NB-IoT (narrowband IoT)devices. Some UEs 120 may be considered a Customer Premises Equipment. AUE 120 may be included inside a housing that houses components of the UE120, such as processor components and/or memory components. In someexamples, the processor components and the memory components may becoupled together. For example, the processor components (e.g., one ormore processors) and the memory components (e.g., a memory) may beoperatively coupled, communicatively coupled, electronically coupled,and/or electrically coupled.

In general, any number of wireless networks 100 may be deployed in agiven geographic area. Each wireless network 100 may support aparticular RAT and may operate on one or more frequencies. A RAT may bereferred to as a radio technology, an air interface, or the like. Afrequency may be referred to as a carrier, a frequency channel, or thelike. Each frequency may support a single RAT in a given geographic areain order to avoid interference between wireless networks of differentRATs. In some cases, NR or 5G RAT networks may be deployed.

In some examples, two or more UEs 120 (e.g., shown as UE 120 a and UE120 e) may communicate directly using one or more sidelink channels(e.g., without using a base station 110 as an intermediary tocommunicate with one another). For example, the UEs 120 may communicateusing peer-to-peer (P2P) communications, device-to-device (D2D)communications, a vehicle-to-everything (V2X) protocol (e.g., which mayinclude a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure(V2I) protocol, or a vehicle-to-pedestrian (V2P) protocol), and/or amesh network. In such examples, a UE 120 may perform schedulingoperations, resource selection operations, and/or other operationsdescribed elsewhere herein as being performed by the base station 110.

Devices of the wireless network 100 may communicate using theelectromagnetic spectrum, which may be subdivided by frequency orwavelength into various classes, bands, channels, or the like. Forexample, devices of the wireless network 100 may communicate using oneor more operating bands. In 5G NR, two initial operating bands have beenidentified as frequency range designations FR1 (410 MHz - 7.125 GHz) andFR2 (24.25 GHz - 52.6 GHz). It should be understood that although aportion of FR1 is greater than 6 GHz, FR1 is often referred to(interchangeably) as a “Sub-6 GHz” band in various documents andarticles. A similar nomenclature issue sometimes occurs with regard toFR2, which is often referred to (interchangeably) as a “millimeter wave”band in documents and articles, despite being different from theextremely high frequency (EHF) band (30 GHz - 300 GHz) which isidentified by the International Telecommunications Union (ITU) as a“millimeter wave” band.

The frequencies between FR1 and FR2 are often referred to as mid-bandfrequencies. Recent 5G NR studies have identified an operating band forthese mid-band frequencies as frequency range designation FR3 (7.125GHz - 24.25 GHz). Frequency bands falling within FR3 may inherit FR1characteristics and/or FR2 characteristics, and thus may effectivelyextend features of FR1 and/or FR2 into mid-band frequencies. Inaddition, higher frequency bands are currently being explored to extend5G NR operation beyond 52.6 GHz. For example, three higher operatingbands have been identified as frequency range designations FR4a or FR4-1(52.6 GHz - 71 GHz), FR4 (52.6 GHz - 114.25 GHz), and FR5 (114.25 GHz -300 GHz). Each of these higher frequency bands falls within the EHFband.

With the above examples in mind, unless specifically stated otherwise,it should be understood that the term “sub-6 GHz” or the like, if usedherein, may broadly represent frequencies that may be less than 6 GHz,may be within FR1, or may include mid-band frequencies. Further, unlessspecifically stated otherwise, it should be understood that the term“millimeter wave” or the like, if used herein, may broadly representfrequencies that may include mid-band frequencies, may be within FR2,FR4, FR4-a or FR4-1, and/or FR5, or may be within the EHF band. It iscontemplated that the frequencies included in these operating bands(e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and/or FR5) may be modified,and techniques described herein are applicable to those modifiedfrequency ranges.

In some aspects, the UE 120 may include a communication manager 140. Asdescribed in more detail elsewhere herein, the communication manager 140may obtain information relating to an environment in which a vehicle islocated; determine using a machine learning model: a driving context ofthe vehicle based at least in part on the information relating to theenvironment, and a set of hyperparameters for a model, that is used todetermine a driving behavior for the vehicle, based at least in part onthe driving context; determine, using the model configured with the setof hyperparameters, the driving behavior for the vehicle; and causeautonomous operation of the vehicle in accordance with the drivingbehavior. Additionally, or alternatively, the communication manager 140may perform one or more other operations described herein.

As indicated above, FIG. 1 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 1 .

FIG. 2 is a diagram illustrating an example 200 of a base station 110 incommunication with a UE 120 in a wireless network 100, in accordancewith the present disclosure. The base station 110 may be equipped with aset of antennas 234 a through 234 t, such as T antennas (T ≥ 1). The UE120 may be equipped with a set of antennas 252 a through 252 r, such asR antennas (R ≥ 1).

At the base station 110, a transmit processor 220 may receive data, froma data source 212, intended for the UE 120 (or a set of UEs 120). Thetransmit processor 220 may select one or more modulation and codingschemes (MCSs) for the UE 120 based at least in part on one or morechannel quality indicators (CQIs) received from that UE 120. The basestation 110 may process (e.g., encode and modulate) the data for the UE120 based at least in part on the MCS(s) selected for the UE 120 and mayprovide data symbols for the UE 120. The transmit processor 220 mayprocess system information (e.g., for semi-static resource partitioninginformation (SRPI)) and control information (e.g., CQI requests, grants,and/or upper layer signaling) and provide overhead symbols and controlsymbols. The transmit processor 220 may generate reference symbols forreference signals (e.g., a cell-specific reference signal (CRS) or ademodulation reference signal (DMRS)) and synchronization signals (e.g.,a primary synchronization signal (PSS) or a secondary synchronizationsignal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO)processor 230 may perform spatial processing (e.g., precoding) on thedata symbols, the control symbols, the overhead symbols, and/or thereference symbols, if applicable, and may provide a set of output symbolstreams (e.g., T output symbol streams) to a corresponding set of modems232 (e.g., T modems), shown as modems 232 a through 232 t. For example,each output symbol stream may be provided to a modulator component(shown as MOD) of a modem 232. Each modem 232 may use a respectivemodulator component to process a respective output symbol stream (e.g.,for OFDM) to obtain an output sample stream. Each modem 232 may furtheruse a respective modulator component to process (e.g., convert toanalog, amplify, filter, and/or upconvert) the output sample stream toobtain a downlink signal. The modems 232 a through 232 t may transmit aset of downlink signals (e.g., T downlink signals) via a correspondingset of antennas 234 (e.g., T antennas), shown as antennas 234 a through234 t.

At the UE 120, a set of antennas 252 (shown as antennas 252 a through252 r) may receive the downlink signals from the base station 110 and/orother base stations 110 and may provide a set of received signals (e.g.,R received signals) to a set of modems 254 (e.g., R modems), shown asmodems 254 a through 254 r. For example, each received signal may beprovided to a demodulator component (shown as DEMOD) of a modem 254.Each modem 254 may use a respective demodulator component to condition(e.g., filter, amplify, downconvert, and/or digitize) a received signalto obtain input samples. Each modem 254 may use a demodulator componentto further process the input samples (e.g., for OFDM) to obtain receivedsymbols. A MIMO detector 256 may obtain received symbols from the modems254, may perform MIMO detection on the received symbols if applicable,and may provide detected symbols. A receive processor 258 may process(e.g., demodulate and decode) the detected symbols, may provide decodeddata for the UE 120 to a data sink 260, and may provide decoded controlinformation and system information to a controller/processor 280. Theterm “controller/processor” may refer to one or more controllers, one ormore processors, or a combination thereof. A channel processor maydetermine a reference signal received power (RSRP) parameter, a receivedsignal strength indicator (RSSI) parameter, a reference signal receivedquality (RSRQ) parameter, and/or a CQI parameter, among other examples.In some examples, one or more components of the UE 120 may be includedin a housing 284.

The network controller 130 may include a communication unit 294, acontroller/processor 290, and a memory 292. The network controller 130may include, for example, one or more devices in a core network. Thenetwork controller 130 may communicate with the base station 110 via thecommunication unit 294.

One or more antennas (e.g., antennas 234 a through 234 t and/or antennas252 a through 252 r) may include, or may be included within, one or moreantenna panels, one or more antenna groups, one or more sets of antennaelements, and/or one or more antenna arrays, among other examples. Anantenna panel, an antenna group, a set of antenna elements, and/or anantenna array may include one or more antenna elements (within a singlehousing or multiple housings), a set of coplanar antenna elements, a setof non-coplanar antenna elements, and/or one or more antenna elementscoupled to one or more transmission and/or reception components, such asone or more components of FIG. 2 .

On the uplink, at the UE 120, a transmit processor 264 may receive andprocess data from a data source 262 and control information (e.g., forreports that include RSRP, RSSI, RSRQ, and/or CQI) from thecontroller/processor 280. The transmit processor 264 may generatereference symbols for one or more reference signals. The symbols fromthe transmit processor 264 may be precoded by a TX MIMO processor 266 ifapplicable, further processed by the modems 254 (e.g., for DFT-s-OFDM orCP-OFDM), and transmitted to the base station 110. In some examples, themodem 254 of the UE 120 may include a modulator and a demodulator. Insome examples, the UE 120 includes a transceiver. The transceiver mayinclude any combination of the antenna(s) 252, the modem(s) 254, theMIMO detector 256, the receive processor 258, the transmit processor264, and/or the TX MIMO processor 266. The transceiver may be used by aprocessor (e.g., the controller/processor 280) and the memory 282 toperform aspects of any of the methods described herein (e.g., withreference to FIGS. 3A-3B, 4, and 5 ).

At the base station 110, the uplink signals from UE 120 and/or other UEsmay be received by the antennas 234, processed by the modem 232 (e.g., ademodulator component, shown as DEMOD, of the modem 232), detected by aMIMO detector 236 if applicable, and further processed by a receiveprocessor 238 to obtain decoded data and control information sent by theUE 120. The receive processor 238 may provide the decoded data to a datasink 239 and provide the decoded control information to thecontroller/processor 240. The base station 110 may include acommunication unit 244 and may communicate with the network controller130 via the communication unit 244. The base station 110 may include ascheduler 246 to schedule one or more UEs 120 for downlink and/or uplinkcommunications. In some examples, the modem 232 of the base station 110may include a modulator and a demodulator. In some examples, the basestation 110 includes a transceiver. The transceiver may include anycombination of the antenna(s) 234, the modem(s) 232, the MIMO detector236, the receive processor 238, the transmit processor 220, and/or theTX MIMO processor 230. The transceiver may be used by a processor (e.g.,the controller/processor 240) and the memory 242 to perform aspects ofany of the methods described herein (e.g., with reference to FIGS.3A-3B, 4, and 5 ).

The controller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform one or more techniques associated with modelhyperparameter adjustment using vehicle driving context classification,as described in more detail elsewhere herein. In some aspects, a systemfor autonomous driving is the UE 120, is included in the UE 120, orincludes one or more components of the UE 120 shown in FIG. 2 . Thecontroller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform or direct operations of, for example, process 400 ofFIG. 4 , and/or other processes as described herein. The memory 242 andthe memory 282 may store data and program codes for the base station 110and the UE 120, respectively. In some examples, the memory 242 and/orthe memory 282 may include a non-transitory computer-readable mediumstoring one or more instructions (e.g., code and/or program code) forwireless communication. For example, the one or more instructions, whenexecuted (e.g., directly, or after compiling, converting, and/orinterpreting) by one or more processors of the base station 110 and/orthe UE 120, may cause the one or more processors, the UE 120, and/or thebase station 110 to perform or direct operations of, for example,process 400 of FIG. 4 , and/or other processes as described herein. Insome examples, executing instructions may include running theinstructions, converting the instructions, compiling the instructions,and/or interpreting the instructions, among other examples.

In some aspects, a system for autonomous driving includes means forobtaining, by a device of a vehicle, information relating to anenvironment in which the vehicle is located; means for determining bythe device using a machine learning model: a driving context of thevehicle based at least in part on the information relating to theenvironment, and a set of hyperparameters for a model, that is used todetermine a driving behavior for the vehicle, based at least in part onthe driving context; means for determining, by the device and using themodel configured with the set of hyperparameters, the driving behaviorfor the vehicle; and/or means for causing, by the device, autonomousoperation of the vehicle in accordance with the driving behavior. Insome aspects, the means for the device to perform operations describedherein may include, for example, one or more of communication manager140, antenna 252, modem 254, MIMO detector 256, receive processor 258,transmit processor 264, TX MIMO processor 266, controller/processor 280,or memory 282.

While blocks in FIG. 2 are illustrated as distinct components, thefunctions described above with respect to the blocks may be implementedin a single hardware, software, or combination component or in variouscombinations of components. For example, the functions described withrespect to the transmit processor 264, the receive processor 258, and/orthe TX MIMO processor 266 may be performed by or under the control ofthe controller/processor 280.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 2 .

An autonomous driving system (e.g., an advanced driver-assistance system(ADAS)) for a vehicle may perform three primary operations: sensing,planning (e.g., what a vehicle is to do), and acting. The planningoperation (e.g., behavioral planning) may include drive policy for thevehicle. One challenge for autonomous driving systems is generalizingbehaviors for different contexts. For example, the parameters for drivepolicy in a free-flowing highway context may be different from theparameters for drive policy at an intersection in a dense urban context.Using independent drive policies for different driving contexts (e.g.,highway, parking lot, or intersection) lacks scalability and consumesexcessive computing resources.

An autonomous driving system for a vehicle may use a model based on ascalable global planner algorithm, such as a Monte Carlo tree search(MCTS) algorithm or another searching algorithm, to determine drivepolicy for the vehicle that maximizes a reward function. The model maybe initialized with a set of hyperparameters (e.g., tree width, treedepth, time step resolution, or the like, in the case of MCTS) that arestatic over the usage of the model in various driving contexts. Thus,the model may be unable to adapt to different driving contexts and mayproduce sub-optimal autonomous operation of the vehicle. Moreover, asdescribed above, the use of multiple models with different sets ofhyperparameters for each driving context lacks scalability to newdriving contexts and consumes excessive computing resources inconnection with storing, switching between, and updating the multiplemodels.

Some techniques and apparatuses described herein provide an autonomousdriving system in which the hyperparameters for a model (e.g., an MCTSmodel), that determines driving behavior for a vehicle, may be adjustedbased on a driving context classification of the vehicle (e.g., ahighway classification, a parking lot classification, an intersectionclassification, or the like). In some aspects, the autonomous drivingsystem may use a machine learning model, such as a neural network, todetermine a set of hyperparameters for the model based on the drivingcontext classification of the vehicle. For example, if the vehicle is ina parking lot, the hyperparameters determined by the machine learningmodel for the model may influence the model to determine drivingbehavior quickly. As another example, if the vehicle is on afree-flowing highway, the hyperparameters determined by the machinelearning model for the model may influence the model to determinedriving behavior further into the future.

Inputs to the machine learning model may include information relating toan environmental model for the vehicle (e.g., a model of objectssurrounding the vehicle that is based on the vehicle’s sensor data)and/or intention predictions of the vehicle (e.g., predictions relatingto the next movements of objects in the environment of the vehicle). Insome aspects, the machine learning model may be trained to identifyvarious driving contexts (e.g., highway, parking lot, intersection, orthe like), and to determine a set of hyperparameters (e.g., tree width,tree depth, or the like) for the model based on the driving context. Insome other aspects, the machine learning model may receive (e.g., fromanother machine learning model) information indicating a driving contextof the vehicle as an input, and the machine learning model may betrained to determine a set of hyperparameters for the model based on thedriving context. In some aspects, the machine learning model may betrained end-to-end with the model (e.g., the MCTS model) in the trainingloop.

In this way, the autonomous driving system may adjust thehyperparameters used by the model based at least in part on a drivingcontext of the vehicle. Accordingly, the hyperparameters may be adjustedin real time or near-real time to adapt to a current driving context ofthe vehicle. Thus, the model for determining driving behavior isscalable across the various driving contexts that may be encountered bythe vehicle, as well as extendable to new driving contexts. Accordingly,the autonomous driving system may use a single model to determinesuitable driving behavior for the vehicle across various drivingcontexts, thereby conserving computing resources that may otherwise beexpended storing, switching between, and updating multiple models.

FIGS. 3A-3B are diagrams illustrating an example 300 of modelhyperparameter adjustment using vehicle driving context classification,in accordance with the present disclosure. As shown, example 300 mayinclude a vehicle 305 that includes a system 310 for autonomous driving.The vehicle 305 may be a car, a bus, a truck, a work machine (e.g., atractor, a bulldozer, or the like), or the like. The system 310 mayinclude one or more computing devices. In some aspects, the system 310may be, or may include, a UE 120.

The system 310 may implement a machine learning model 312 that istrained to determine a set of hyperparameters for a model 314 used todetermine a driving behavior for the vehicle 305. For example, themachine learning model 312 may be trained to determine the set ofhyperparameters based at least in part on a driving context of thevehicle 305, as described herein. In particular, the machine learningmodel 312 may be trained to determine the set of hyperparameters basedat least in part on an input of environmental information (e.g., cameradata, light detection and ranging (LIDAR) data, or the like) associatedwith the vehicle 305. In some aspects, the machine learning model 312may be a neural network (e.g., a feedforward neural network, aconvolutional neural network, or the like).

In some aspects, the system 310 may train the machine learning model312. The system 310 may train the machine learning model 312 alone, orin cooperation with similar systems of one or more other vehicles (e.g.,using distributed learning, federated learning, or the like). In someother aspects, the system 310 may receive the trained machine learningmodel 312 from another device or may otherwise be provisioned with thetrained machine learning model 312. In some aspects, the machinelearning model 312 may be trained using a reinforcement learningtechnique. The training data for the machine learning model 312 mayinclude environmental information for a vehicle, such as camera data,LIDAR data, or the like. Moreover, the machine learning model 312 may betrained based at least in part on an output of the model 314 (e.g., aloss function for training the machine learning model 312 may be basedat least in part on an output of the model 314).

In practice, the vehicle 305 may be located in an environment thatincludes one or more objects. For example, the objects may include oneor more other vehicles and/or one or more pedestrians, among otherexamples. As shown in FIG. 3A, and by reference number 315, the system310 may obtain information relating to the environment (e.g., obtain anenvironmental model) in which the vehicle 305 is located (referred toherein as “environment information”). For example, the system 310 mayobtain the environment information from one or more sensors of thevehicle 305. The one or more sensors may include one or more LIDARsystems of the vehicle 305, one or more cameras of the vehicle 305,and/or one or more radio detection and ranging (RADAR) systems of thevehicle 305, among other examples. In some aspects, the environmentinformation may include location data associated with the vehicle 305,map data associated with the location of the vehicle 305, traffic dataassociated with the vehicle 305, condition data (e.g., weather data, caraccident data, lane closure data, or the like) associated with thelocation of the vehicle 305, or the like. The system 310 may obtain thelocation data from a sensor of the vehicle 305 (e.g., a globalnavigation satellite system (GNSS) of the vehicle 305, such as a globalpositioning system (GPS) of the vehicle 305). The system 310 may obtainthe map data, the traffic data, and/or the condition data from a storagesystem of the vehicle 305, from another vehicle (e.g., using V2Xcommunication on a sidelink), from a server device (e.g., viacommunication using wireless network 100), or the like.

As shown by reference number 320, the system 310 may determine one ormore intention predictions. The one or more intention predictions mayrelate to one or more objects (e.g., other vehicles, pedestrians,animals, or the like) in the environment of the vehicle 305. The system310 may identify the one or more objects from the environmentinformation (e.g., using a computer vision technique, an objectdetection technique, or the like). The system 310 may determine the oneor more intention predictions using a different machine learning model.An intention prediction for an object may be a prediction of a futuremovement (e.g., movement direction, movement speed, or the like) of theobject.

As shown by reference number 325, the system 310 may provide (e.g., asan input) the environment information and/or the one or more intentionpredictions to the machine learning model 312. As shown, the system 310may implement the machine learning model 312. However, in some aspects,the machine learning model 312 may be implemented by a remote devicefrom the system 310 (e.g., by a remote server device, by a cloudcomputing environment, by a roadside unit, or the like). Here, toprovide the environment information and/or the intention prediction(s)to the machine learning model 312, the system 310 may transmit theenvironment information and/or the intention prediction(s) to the remotedevice (e.g., via wireless network 100).

As shown in FIG. 3B, and by reference number 330, the system 310 maydetermine a set of hyperparameters for the model 314. The system 310 maydetermine the set of hyperparameters for the model 314 based at least inpart on the environment information and/or the intention prediction(s).In particular, the system 310 may determine the set of hyperparametersfor the model 314 based at least in part on a driving context of thevehicle that is indicated by the environment information and/or theintention prediction(s). Thus, the system 310 may determine a first setof hyperparameters for the model 314 responsive to a first drivingcontext, the system 310 may determine a second set of hyperparametersfor the model 314 responsive to a second driving context, and so forth.For example, for a highway driving context, the system 310 may determinea relatively greater tree depth for the model 314, and for a parking lotcontext, the system 310 may determine a relatively lesser tree depth forthe model 314.

In some aspects, the system 310 may determine the set of hyperparametersfor the model 314 continuously (e.g., at a same frequency at which themodel 314 determines driving behavior). In some aspects, the system 310may determine the set of hyperparameters for the model 314 periodically(e.g., at a slower frequency than at which the model 314 determinesdriving behavior). In some aspects, the system 310 may determine the setof hyperparameters for the model 314 based at least in part on detectingthe occurrence of an event. For example, the system 310 may determinethe set of hyperparameters for the model 314 based at least in part ondetecting (e.g., using a machine learning model trained to performdriving context classification, as described herein) that a drivingcontext of the vehicle 305 has changed. In some aspects, the system 310may store information indicating a route that is to be taken by thevehicle 305. Here, the system 310 may determine locations at which thedriving context of the vehicle 305 will change based at least in part onthe route, and the system 310 may determine the set of hyperparameterswhen the vehicle 305 is at, or is approaching, a location at which thedriving context of the vehicle will change.

The system 310 may determine the set of hyperparameters for the model314 using the machine learning model 312. For example, the system 310,using the machine learning model 312, may determine a driving context(e.g., a driving context classification) of the vehicle 305 based atleast in part on the environment information and/or the intentionprediction(s), and determine a set of hyperparameters for the model 314based at least in part on the driving context. In other words, themachine learning model 312 may output the set of hyperparameters for themodel 314 responsive to an input to the machine learning model 312 ofthe environment information and/or the intention prediction(s).

In some aspects, the machine learning model 312 may be trained todetermine the set of hyperparameters based at least in part on a featureset relating to characteristics of the environment information and/orthe intention prediction(s) (e.g., the feature set may include aquantity of objects in the environment, distances of objects in theenvironment from the vehicle 305, speeds of objects in the environment,orientations of objects in the environment relative to the vehicle 305,movements of objects in the environment relative to the vehicle 305,configurations of one or more roads in the environment, traffic signalsin the environment, and/or traffic signs in the environment, among otherexamples). In some aspects, the machine learning model 312 may betrained to determine a driving context classification based at least inpart on a feature set relating to characteristics of the environmentinformation and/or the intention prediction(s), and the machine learningmodel 312 may be trained to determine the set of hyperparameters basedat least in part on the driving context classification and/or theintention prediction(s). In some aspects, the system 310, using adifferent machine learning model, may determine the driving contextclassification based at least in part on the environment informationand/or the intention prediction(s), and the machine learning model 312may be trained to determine the set of hyperparameters based at least inpart on the driving context classification and/or the intentionprediction(s). In some aspects, the system 310 may determine the drivingcontext classification, as described herein, and the system 310 may usea mapping (e.g., stored by the system 310) of driving contextclassifications to sets of hyperparameters in order to determine the setof hyperparameters. In some aspects, the system 310 may determine thedriving context classification based at least in part on informationindicating a route that is to be taken by the vehicle 305, as describedherein.

A driving context may relate to a current driving scenario in which thevehicle 305 is involved. For example, a driving context may relate to atype of road on which the vehicle 305 is traveling, a type ofintersection that the vehicle 305 is approaching, a driving maneuverthat the vehicle 305 is performing, or the like. As an example, thedriving context may be an intersection context, a traffic signalcontext, a highway context, a local road context, a one-way roadcontext, a parking lot context, a parking context, a parallel parkingcontext, or a merging context, among other examples. In some aspects,the set of hyperparameters determined by the system 310, based at leastin part on the driving context, may include (e.g., for a heuristicsearch algorithm) a tree width, a tree depth, a time length for a stepin a tree, a reward function, and/or a scope of an action space, amongother examples.

In some aspects, the system 310 may determine the set of hyperparametersusing cooperation between the vehicle 305 and one or more differentvehicles. In particular, a vehicle arriving at a location or areaassociated with a particular driving context may determinehyperparameters based at least in part on the driving context, asdescribed herein, and the vehicle may transmit information indicatingthe hyperparameters that are determined to one or more neighboringvehicles (e.g., particularly neighboring vehicles that are about toarrive at the location or the area). The information may also indicate afeedback value (e.g., a loss function value, a reward function value, orthe like) that quantifies an accuracy and/or an optimality associatedwith a driving behavior that was determined by the vehicle based atleast in part on the hyperparameters, as described herein. For example,the system 310 may receive (e.g., using V2X communication on a sidelink)information indicating hyperparameters determined at a different vehiclefor a particular location or area and/or a feedback value. Continuingwith the example, the system 310 may use the hyperparameters determinedat the different vehicle, or the system 310 may determine (e.g., usingthe machine learning model 312) the set of hyperparameters based atleast in part on the hyperparameters determined at the different vehicleand/or the feedback value. For example, the hyperparameters determinedat the different vehicle and/or the feedback value may be additionalinputs to the machine learning model 312. In this way, vehicles maycooperate to facilitate hyperparameter determination that improves overtime.

As shown by reference number 335, the system 310 may configure the model314 with the set of hyperparameters. That is, the system 310 mayinitialize the model 314 using the set of hyperparameters. In someaspects, the model 314 may be based at least in part on a heuristicsearch algorithm (e.g., a scalable global planner algorithm). Forexample, the model 314 may be based at least in part on an MCTS. Thatis, the model 314 may employ an MCTS algorithm.

As shown by reference number 340, the system 310 may determine a drivingbehavior (e.g., drive policy) for the vehicle 305. For example, thesystem 310 may determine the driving behavior for the vehicle 305 usingthe model 314 configured with the set of hyperparameters. As an example,the system 310 may determine the driving behavior by executing the MCTSalgorithm with respect to the set of hyperparameters (e.g., by executingthe MCTS algorithm using a particular tree width indicated by the set ofhyperparameters, using a particular tree depth indicated by the set ofhyperparameters, and so forth). The model 314 may be configured tooutput the driving behavior responsive to an input of input information,such as the environment information (e.g., an environmental model), theintention prediction(s), or the like. Moreover, the system 310 maydetermine the driving behavior, using the model 314, so as to maximize areward function for the model 314. The driving behavior may relate to anaction of the vehicle 305 (e.g., braking, accelerating, turning, or thelike), a speed of the vehicle 305, and/or a path of the vehicle 305.

As shown by reference number 345, the system 310 may cause autonomousoperation (e.g., operation without human input) of the vehicle 305 inaccordance with the driving behavior. For example, the system 310 maycause one or more systems of the vehicle 305 (e.g., a steering system, apropulsion system, a braking system, or the like) to perform one or moreoperations in accordance with the driving behavior.

In this way, the model 314 may determine suitable driving behavior fordifferent driving contexts. In particular, the model 314 may be adaptedto different driving contexts by adjusting, using the machine learningmodel 312, the set of hyperparameters used by the model 314, asdescribed herein. Thus, the model 314 may provide the same functionalityas multiple different models that are separately configured to handledifferent driving contexts. Accordingly, the system 310 facilitatesscalability of the model 114 to different driving contexts, andconserves computing resources that would otherwise be expended onstoring, switching between, and updating multiple different models.

As indicated above, FIGS. 3A-3B are provided as an example. Otherexamples may differ from what is described with respect to FIGS. 3A-3B.

FIG. 4 is a diagram illustrating an example process 400 performed, forexample, by a device, in accordance with the present disclosure. Exampleprocess 400 is an example where the device (e.g., system 310, UE 120, orthe like) performs operations associated with model hyperparameteradjustment using vehicle driving context classification.

As shown in FIG. 4 , in some aspects, process 400 may include obtaininginformation relating to an environment in which a vehicle is located(block 410). For example, the device (e.g., using communication manager508 and/or sensing component 510, depicted in FIG. 5 ) may obtaininformation relating to an environment in which the vehicle is located,as described above.

As further shown in FIG. 4 , in some aspects, process 400 may includedetermining using a machine learning model: a driving context of thevehicle based at least in part on the information relating to theenvironment, and a set of hyperparameters for a model, that is used todetermine a driving behavior for the vehicle, based at least in part onthe driving context (block 420). For example, the device (e.g., usingcommunication manager 508 and/or determination component 512, depictedin FIG. 5 ) may determine using a machine learning model: a drivingcontext of the vehicle based at least in part on the informationrelating to the environment, and a set of hyperparameters for a model,that is used to determine a driving behavior for the vehicle, based atleast in part on the driving context, as described above.

As further shown in FIG. 4 , in some aspects, process 400 may includedetermining, using the model configured with the set of hyperparameters,the driving behavior for the vehicle (block 430). For example, thedevice (e.g., using communication manager 508 and/or determinationcomponent 512, depicted in FIG. 5 ) may determine, using the modelconfigured with the set of hyperparameters, the driving behavior for thevehicle, as described above.

As further shown in FIG. 4 , in some aspects, process 400 may includecausing autonomous operation of the vehicle in accordance with thedriving behavior (block 440). For example, the device (e.g., usingcommunication manager 508 and/or acting component 514, depicted in FIG.5 ) may cause autonomous operation of the vehicle in accordance with thedriving behavior, as described above.

Process 400 may include additional aspects, such as any single aspect orany combination of aspects described below and/or in connection with oneor more other processes described elsewhere herein.

In a first aspect, the model is based at least in part on a heuristicsearch algorithm.

In a second aspect, alone or in combination with the first aspect, themodel is based at least in part on a Monte Carlo tree search.

In a third aspect, alone or in combination with one or more of the firstand second aspects, the set of hyperparameters includes at least one ofa tree width, a tree depth, a time length for a step in a tree, or areward function.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, the machine learning model is a neuralnetwork trained to determine hyperparameters for the model based atleast in part on driving context.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the driving context of the vehicle is anintersection context, a highway context, a local road context, or aparking lot context.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the information relating to the environment isobtained from one or more sensors of the vehicle.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the one or more sensors include one or moreof a light detection and ranging system or a camera.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, the driving behavior is determined tomaximize a reward function for the model.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, process 400 includes determining one or moreintention predictions relating to one or more objects in theenvironment, where at least one of the driving context or the set ofhyperparameters is determined further based at least in part on the oneor more intention predictions.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, process 400 includes configuring the model withthe set of hyperparameters based at least in part on determining the setof hyperparameters.

Although FIG. 4 shows example blocks of process 400, in some aspects,process 400 may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in FIG. 4 .Additionally, or alternatively, two or more of the blocks of process 400may be performed in parallel.

FIG. 5 is a diagram of an example apparatus 500. The apparatus 500 maybe a system for autonomous driving, or a system for autonomous drivingmay include the apparatus 500. In some aspects, the apparatus 500includes a reception component 502 and a transmission component 504,which may be in communication with one another (for example, via one ormore buses and/or one or more other components). As shown, the apparatus500 may communicate with another apparatus 506 (such as a UE, a basestation, or another wireless communication device) using the receptioncomponent 502 and the transmission component 504. As further shown, theapparatus 500 may include the communication manager 508. Thecommunication manager 508 may be, may include, or may be similar to thecommunication manager 140. The communication manager 508 may include oneor more of a sensing component 510, a determination component 512, or anacting component 514, among other examples.

In some aspects, the apparatus 500 may be configured to perform one ormore operations described herein in connection with FIGS. 3A-3B.Additionally, or alternatively, the apparatus 500 may be configured toperform one or more processes described herein, such as process 400 ofFIG. 4 , or a combination thereof. In some aspects, the apparatus 500and/or one or more components shown in FIG. 5 may include one or morecomponents of the system described in connection with FIG. 2 .Additionally, or alternatively, one or more components shown in FIG. 5may be implemented within one or more components described in connectionwith FIG. 2 . Additionally, or alternatively, one or more components ofthe set of components may be implemented at least in part as softwarestored in a memory. For example, a component (or a portion of acomponent) may be implemented as instructions or code stored in anon-transitory computer-readable medium and executable by a controlleror a processor to perform the functions or operations of the component.

The reception component 502 may receive communications, such asreference signals, control information, data communications, or acombination thereof, from the apparatus 506. The reception component 502may provide received communications to one or more other components ofthe apparatus 500. In some aspects, the reception component 502 mayperform signal processing on the received communications (such asfiltering, amplification, demodulation, analog-to-digital conversion,demultiplexing, deinterleaving, de-mapping, equalization, interferencecancellation, or decoding, among other examples), and may provide theprocessed signals to the one or more other components of the apparatus500. In some aspects, the reception component 502 may include one ormore antennas, a modem, a demodulator, a MIMO detector, a receiveprocessor, a controller/processor, a memory, or a combination thereof,of the system described in connection with FIG. 2 .

The transmission component 504 may transmit communications, such asreference signals, control information, data communications, or acombination thereof, to the apparatus 506. In some aspects, one or moreother components of the apparatus 500 may generate communications andmay provide the generated communications to the transmission component504 for transmission to the apparatus 506. In some aspects, thetransmission component 504 may perform signal processing on thegenerated communications (such as filtering, amplification, modulation,digital-to-analog conversion, multiplexing, interleaving, mapping, orencoding, among other examples), and may transmit the processed signalsto the apparatus 506. In some aspects, the transmission component 504may include one or more antennas, a modem, a modulator, a transmit MIMOprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the system described in connection with FIG. 2 .In some aspects, the transmission component 504 may be co-located withthe reception component 502 in a transceiver.

The sensing component 510 may obtain information relating to anenvironment in which a vehicle is located. The determination component512 may determine, using a machine learning model, a driving context ofthe vehicle based at least in part on the information relating to theenvironment, and a set of hyperparameters for a model, that is used todetermine a driving behavior for the vehicle, based at least in part onthe driving context. The determination component 512 may determine,using the model configured with the set of hyperparameters, the drivingbehavior for the vehicle. The acting component 514 may cause autonomousoperation of the vehicle in accordance with the driving behavior.

The determination component 512 may determine one or more intentionpredictions relating to one or more objects in the environment. In someaspects, at least one of the driving context or the set ofhyperparameters is determined further based at least in part on the oneor more intention predictions. The determination component 512 mayconfigure the model with the set of hyperparameters based at least inpart on determining the set of hyperparameters.

The number and arrangement of components shown in FIG. 5 are provided asan example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 5 . Furthermore, two or more components shownin FIG. 5 may be implemented within a single component, or a singlecomponent shown in FIG. 5 may be implemented as multiple, distributedcomponents. Additionally, or alternatively, a set of (one or more)components shown in FIG. 5 may perform one or more functions describedas being performed by another set of components shown in FIG. 5 .

The following provides an overview of some Aspects of the presentdisclosure:

Aspect 1: A method, comprising: obtaining, by a device of a vehicle,information relating to an environment in which the vehicle is located;determining by the device using a machine learning model: a drivingcontext of the vehicle based at least in part on the informationrelating to the environment, and a set of hyperparameters for a model,that is used to determine a driving behavior for the vehicle, based atleast in part on the driving context; determining, by the device andusing the model configured with the set of hyperparameters, the drivingbehavior for the vehicle; and causing, by the device, autonomousoperation of the vehicle in accordance with the driving behavior.

Aspect 2: The method of Aspect 1, wherein the model is based at least inpart on a heuristic search algorithm.

Aspect 3: The method of any of Aspects 1-2, wherein the model is basedat least in part on a Monte Carlo tree search.

Aspect 4: The method of any of Aspects 1-3, wherein the set ofhyperparameters includes at least one of: a tree width, a tree depth, atime length for a step in a tree, or a reward function.

Aspect 5: The method of any of Aspects 1-4, wherein the machine learningmodel is a neural network trained to determine hyperparameters for themodel based at least in part on driving context.

Aspect 6: The method of any of Aspects 1-5, wherein the driving contextof the vehicle is an intersection context, a highway context, a localroad context, or a parking lot context.

Aspect 7: The method of any of Aspects 1-6, wherein the informationrelating to the environment is obtained from one or more sensors of thevehicle.

Aspect 8: The method of Aspect 7, wherein the one or more sensorsinclude one or more of: a light detection and ranging system or acamera.

Aspect 9: The method of any of Aspects 1-8, wherein the driving behavioris determined to maximize a reward function for the model.

Aspect 10: The method of any of Aspects 1-9, further comprising:determining one or more intention predictions relating to one or moreobjects in the environment, wherein at least one of the driving contextor the set of hyperparameters is determined further based at least inpart on the one or more intention predictions.

Aspect 11: The method of any of Aspects 1-10, further comprising:configuring the model with the set of hyperparameters based at least inpart on determining the set of hyperparameters.

Aspect 12: An apparatus at a device, comprising a processor; memorycoupled with the processor; and instructions stored in the memory andexecutable by the processor to cause the apparatus to perform the methodof one or more of Aspects 1-11.

Aspect 13: A device, comprising a memory and one or more processorscoupled to the memory, the one or more processors configured to performthe method of one or more of Aspects 1-11.

Aspect 14: An apparatus, comprising at least one means for performingthe method of one or more of Aspects 1-11.

Aspect 15: A non-transitory computer-readable medium storing code, thecode comprising instructions executable by a processor to perform themethod of one or more of Aspects 1-11.

Aspect 16: A non-transitory computer-readable medium storing a set ofinstructions, the set of instructions comprising one or moreinstructions that, when executed by one or more processors of a device,cause the device to perform the method of one or more of Aspects 1-11.

The foregoing disclosure provides illustration and description but isnot intended to be exhaustive or to limit the aspects to the preciseforms disclosed. Modifications and variations may be made in light ofthe above disclosure or may be acquired from practice of the aspects.

As used herein, the term “component” is intended to be broadly construedas hardware and/or a combination of hardware and software. “Software”shall be construed broadly to mean instructions, instruction sets, code,code segments, program code, programs, subprograms, software modules,applications, software applications, software packages, routines,subroutines, objects, executables, threads of execution, procedures,and/or functions, among other examples, whether referred to as software,firmware, middleware, microcode, hardware description language, orotherwise. As used herein, a “processor” is implemented in hardwareand/or a combination of hardware and software. It will be apparent thatsystems and/or methods described herein may be implemented in differentforms of hardware and/or a combination of hardware and software. Theactual specialized control hardware or software code used to implementthese systems and/or methods is not limiting of the aspects. Thus, theoperation and behavior of the systems and/or methods are describedherein without reference to specific software code, since those skilledin the art will understand that software and hardware can be designed toimplement the systems and/or methods based, at least in part, on thedescription herein.

As used herein, “satisfying a threshold” may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various aspects. Many of thesefeatures may be combined in ways not specifically recited in the claimsand/or disclosed in the specification. The disclosure of various aspectsincludes each dependent claim in combination with every other claim inthe claim set. As used herein, a phrase referring to “at least one of” alist of items refers to any combination of those items, including singlemembers. As an example, “at least one of: a, b, or c” is intended tocover a, b, c, a + b, a + c, b + c, and a + b + c, as well as anycombination with multiples of the same element (e.g., a + a, a + a + a,a + a + b, a + a + c, a + b + b, a + c + c, b + b, b + b + b, b + b + c,c + c, and c + c + c, or any other ordering of a, b, and c).

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterms “set” and “group” are intended to include one or more items andmay be used interchangeably with “one or more.” Where only one item isintended, the phrase “only one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms that do not limit an element that they modify(e.g., an element “having” A may also have B). Further, the phrase“based on” is intended to mean “based, at least in part, on” unlessexplicitly stated otherwise. Also, as used herein, the term “or” isintended to be inclusive when used in a series and may be usedinterchangeably with “and/or,” unless explicitly stated otherwise (e.g.,if used in combination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: obtaining, by a device of a vehicle, information relating to an environment in which the vehicle is located; determining by the device using a machine learning model: a driving context of the vehicle based at least in part on the information relating to the environment, and a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the driving context; determining, by the device and using the model configured with the set of hyperparameters, the driving behavior for the vehicle; and causing, by the device, autonomous operation of the vehicle in accordance with the driving behavior.
 2. The method of claim 1, wherein the model is based at least in part on a heuristic search algorithm.
 3. The method of claim 1, wherein the model is based at least in part on a Monte Carlo tree search.
 4. The method of claim 1, wherein the set of hyperparameters includes at least one of: a tree width, a tree depth, a time length for a step in a tree, or a reward function.
 5. The method of claim 1, wherein the machine learning model is a neural network trained to determine hyperparameters for the model based at least in part on driving context.
 6. The method of claim 1, wherein the driving context of the vehicle is an intersection context, a highway context, a local road context, or a parking lot context.
 7. The method of claim 1, wherein the information relating to the environment is obtained from one or more sensors of the vehicle.
 8. The method of claim 7, wherein the one or more sensors include one or more of: a light detection and ranging system or a camera.
 9. The method of claim 1, wherein the driving behavior is determined to maximize a reward function for the model.
 10. The method of claim 1, further comprising: determining one or more intention predictions relating to one or more objects in the environment, wherein at least one of the driving context or the set of hyperparameters is determined further based at least in part on the one or more intention predictions.
 11. The method of claim 1, further comprising: configuring the model with the set of hyperparameters based at least in part on determining the set of hyperparameters.
 12. A system for autonomous driving of a vehicle, comprising: a memory; and one or more processors, coupled to the memory, configured to: obtain information relating to an environment in which the vehicle is located; determine using a machine learning model: a driving context of the vehicle based at least in part on the information relating to the environment, and a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the driving context; determine, using the model configured with the set of hyperparameters, the driving behavior for the vehicle; and cause autonomous operation of the vehicle in accordance with the driving behavior.
 13. The system of claim 12, wherein the model is based at least in part on a heuristic search algorithm.
 14. The system of claim 12, wherein the model is based at least in part on a Monte Carlo tree search.
 15. The system of claim 12, wherein the set of hyperparameters includes at least one of: a tree width, a tree depth, a time length for a step in a tree, or a reward function.
 16. The system of claim 12, wherein the machine learning model is a neural network trained to determine hyperparameters for the model based at least in part on driving context.
 17. The system of claim 12, wherein the driving context of the vehicle is an intersection context, a highway context, a local road context, or a parking lot context.
 18. The system of claim 12, wherein the information relating to the environment is obtained from one or more sensors of the vehicle.
 19. The system of claim 18, wherein the one or more sensors include one or more of: a light detection and ranging system or a camera.
 20. The system of claim 12, wherein the driving behavior is determined to maximize a reward function for the model.
 21. The system of claim 12, wherein the one or more processors are further configured to: determine one or more intention predictions relating to one or more objects in the environment, wherein at least one of the driving context or the set of hyperparameters is determined further based at least in part on the one or more intention predictions.
 22. The system of claim 12, wherein the one or more processors are further configured to: configure the model with the set of hyperparameters based at least in part on determining the set of hyperparameters.
 23. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: obtain information relating to an environment in which a vehicle is located; determine using a machine learning model: a driving context of the vehicle based at least in part on the information relating to the environment, and a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the driving context; and configure the model with the set of hyperparameters based at least in part on determining the set of hyperparameters.
 24. The non-transitory computer-readable medium of claim 23, wherein the set of hyperparameters includes at least one of: a tree width, a tree depth, a time length for a step in a tree, or a reward function.
 25. The non-transitory computer-readable medium of claim 23, wherein the driving context of the vehicle is an intersection context, a highway context, a local road context, or a parking lot context.
 26. The non-transitory computer-readable medium of claim 23, wherein the one or more instructions further cause the device to: determine one or more intention predictions relating to one or more objects in the environment, wherein at least one of the driving context or the set of hyperparameters is determined further based at least in part on the one or more intention predictions.
 27. An apparatus, comprising: means for obtaining information relating to an environment in which a vehicle is located; means for determining, using a machine learning model, a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the information relating to the environment; and means for configuring the model with the set of hyperparameters based at least in part on determining the set of hyperparameters.
 28. The apparatus of claim 27, wherein the set of hyperparameters includes at least one of: a tree width, a tree depth, a time length for a step in a tree, or a reward function.
 29. The apparatus of claim 27, further comprising: means for determining one or more intention predictions relating to one or more objects in the environment, wherein the set of hyperparameters is determined further based at least in part on the one or more intention predictions.
 30. The apparatus of claim 27, further comprising: means for configuring the model with the set of hyperparameters based at least in part on determining the set of hyperparameters. 